Data streaming with affinity propagation

Xiangliang Zhang; Cyril Furtlehner; Michèle Sebag

doi:10.1007/978-3-540-87481-2_41

Data streaming with affinity propagation

Xiangliang Zhang^*, Cyril Furtlehner, Michèle Sebag

^*Corresponding author for this work

Computer, Electrical and Mathematical Sciences and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

73 Scopus citations

Abstract

This paper proposed StrAP (Streaming AP), extending Affinity Propagation (AP) to data steaming. AP, a new clustering algorithm, extracts the data items, or exemplars, that best represent the dataset using a message passing method. Several steps are made to build StrAP. The first one (Weighted AP) extends AP to weighted items with no loss of generality. The second one (Hierarchical WAP) is concerned with reducing the quadratic AP complexity, by applying AP on data subsets and further applying Weighted AP on the exemplars extracted from all subsets. Finally StrAP extends Hierarchical WAP to deal with changes in the data distribution. Experiments on artificial datasets, on the Intrusion Detection benchmark (KDD99) and on a real-world problem, clustering the stream of jobs submitted to the EGEE grid system, provide a comparative validation of the approach. © 2008 Springer-Verlag Berlin Heidelberg.

Original language	English (US)
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings
Pages	628-643
Number of pages	16
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-540-87481-2_41 https://doi.org/10.1007/978-3-540-87481-2_41
State	Published - Nov 19 2008
Event	European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008 - Antwerp, Belgium Duration: Sep 15 2008 → Sep 19 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	5212 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008
Country/Territory	Belgium
City	Antwerp
Period	09/15/08 → 09/19/08

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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Zhang, X., Furtlehner, C., & Sebag, M. (2008). Data streaming with affinity propagation. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings (PART 2 ed., pp. 628-643). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5212 LNAI, No. PART 2). https://doi.org/10.1007/978-3-540-87481-2_41, https://doi.org/10.1007/978-3-540-87481-2_41

Zhang, Xiangliang ; Furtlehner, Cyril ; Sebag, Michèle. / Data streaming with affinity propagation. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings. PART 2. ed. 2008. pp. 628-643 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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Zhang, X, Furtlehner, C & Sebag, M 2008, Data streaming with affinity propagation. in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5212 LNAI, pp. 628-643, European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008, Antwerp, Belgium, 09/15/08. https://doi.org/10.1007/978-3-540-87481-2_41, https://doi.org/10.1007/978-3-540-87481-2_41

Data streaming with affinity propagation. / Zhang, Xiangliang; Furtlehner, Cyril; Sebag, Michèle.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings. PART 2. ed. 2008. p. 628-643 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5212 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2008/11/19

Y1 - 2008/11/19

N2 - This paper proposed StrAP (Streaming AP), extending Affinity Propagation (AP) to data steaming. AP, a new clustering algorithm, extracts the data items, or exemplars, that best represent the dataset using a message passing method. Several steps are made to build StrAP. The first one (Weighted AP) extends AP to weighted items with no loss of generality. The second one (Hierarchical WAP) is concerned with reducing the quadratic AP complexity, by applying AP on data subsets and further applying Weighted AP on the exemplars extracted from all subsets. Finally StrAP extends Hierarchical WAP to deal with changes in the data distribution. Experiments on artificial datasets, on the Intrusion Detection benchmark (KDD99) and on a real-world problem, clustering the stream of jobs submitted to the EGEE grid system, provide a comparative validation of the approach. © 2008 Springer-Verlag Berlin Heidelberg.

AB - This paper proposed StrAP (Streaming AP), extending Affinity Propagation (AP) to data steaming. AP, a new clustering algorithm, extracts the data items, or exemplars, that best represent the dataset using a message passing method. Several steps are made to build StrAP. The first one (Weighted AP) extends AP to weighted items with no loss of generality. The second one (Hierarchical WAP) is concerned with reducing the quadratic AP complexity, by applying AP on data subsets and further applying Weighted AP on the exemplars extracted from all subsets. Finally StrAP extends Hierarchical WAP to deal with changes in the data distribution. Experiments on artificial datasets, on the Intrusion Detection benchmark (KDD99) and on a real-world problem, clustering the stream of jobs submitted to the EGEE grid system, provide a comparative validation of the approach. © 2008 Springer-Verlag Berlin Heidelberg.

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Zhang X, Furtlehner C, Sebag M. Data streaming with affinity propagation. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings. PART 2 ed. 2008. p. 628-643. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-540-87481-2_41, 10.1007/978-3-540-87481-2_41

Data streaming with affinity propagation

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